Algorithm Design and Analysis Semester 2/2024

Lecture: F. 13.00-16.00
Room: 17304
Class code: qegwxpq
Instructor: Vacharapat Mettanant
Prerequisites:  

• 03603211 Discrete Mathematics

• 03603212 Abstract Datatypes and Problem Solving

Book: The primary written references for the course are:

• Cormen, Thomas H., Leiserson, Charles E., Rivest, Ronald L. and Stein, Clifford. Introduction to Algorithms. 4rd ed. MIT Press, 2022. [MIT Press]

• Kleinberg, Jon and Tardos, Éva. Algorithm Design. : Addison Wesley, 2006. [Pearson]

Course Learning Outcomes: Students who pass this course will have ability to

• Argue the correctness of algorithms using inductive proofs and invariants.

• Analyze worst-case running times of algorithms using asymptotic analysis.

• Describe algorithm design paradigms such as greedy algorithms, dynamic programming, divide and conquer, and randomized algorithms, and explain when an algorithmic design situation calls for them. Recite algorithms that employ these paradigms. Synthesize these algorithms, and analyze them.

Learning activities: This course is an experiment of active learning classes in the area of theoretical computer science. We will have many active learning activities such as group discussion, presentation, or brainstorm. 

Scores: 

Online quizzes 10%

Assignments 20%

Programming Tasks 20%

Midterm 25%

Final 25%

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